How to choose and implement a machine learning platform?
A machine learning platform should support the end-to-end data science and machine learning lifecycle, facilitate collaboration between data analysts and data scientists, and enable the MLOps process. The main capabilities of the AI platform should include data ingestion, data preparation, and data exploration. It should also include feature selection, feature engineering, prototyping, experimentation, model training, validation, model testing, deployment to production, model serving, and monitoring.
A good platform should support a variety of machine learning algorithms including predictive analytics, deep learning, reinforcement learning, and the creation of various types of neural networks, etc. A data science and machine learning platform is typically an extension of an enterprise data analytics platform and should support a variety of integrations.
There are a variety of product vendors offering software as a service solutions. All major cloud providers have their own data science platform offerings. Good open source-based options exist too. Different options may work best for different companies, depending on their machine learning use cases, the maturity of the team, whether they are in the datacenter or in the cloud, and what cloud provider they’ve selected.Our focus is on making the right choice for the right circumstances. We go beyond the deployment of the AI platform. We help you choose the right one, integrate it with the data lake or analytics platform, make the data available, onboard the MLOps process, train data scientists, implement a common library of machine learning models, and ensure that the data science process works smoothly from data to insights.
We have developed advanced artificial intelligence use cases, machine learning platforms, and onboard MLOps processes for Fortune-1000 enterprises across various industries including telecom, retail, media, gaming, and financial services.
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We provide flexible engagement options to design and build ML platforms and artificial intelligence use cases, and onboard the MLOps process and culture. Contact us today to get started with a workshop, discovery, or PoC.
We offer free half-day workshops with our top experts in ML platforms and MLOps and real-time analytics to discuss your stream processing strategy, challenges, optimization opportunities, and industry best practices.
If you have already identified a need to improve the machine learning process and onboard an ML platform, we can start with a 4–8-week proof-of-concept project to deliver tangible results for your enterprise.
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